class_docstr: Implementation of the model H/ASP without batch normalization in speaker embedding. This model was proposed in: https://arxiv.org/abs/2009.14153
  Adapted from: https://github.com/clovaai/voxceleb_trainer
class_docstr_tok: ["Implementation","of","the","model","H\/ASP","without","batch","normalization","in","speaker","embedding",".","This","model","was","proposed","in",":","https",":","\/\/arxiv.org\/abs\/2009.14153","Adapted","from",":","https",":","\/\/github.com\/clovaai\/voxceleb_trainer"]
code: def create_layer(self, block, planes, blocks, stride=1):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(planes * block.expansion),
          )
  
      layers = []
      layers.append(block(self.inplanes, planes, stride, downsample))
      self.inplanes = planes * block.expansion
      for _ in range(1, blocks):
          layers.append(block(self.inplanes, planes))
  
      return nn.Sequential(*layers)
  
file_path: TTS/TTS/encoder/models/resnet.py
imports: import torch
  from torch import nn
  from TTS.encoder.models.base_encoder import BaseEncoder
label_desc: Use image node_id 3 for calling the ResNetSpeakerEncoder obj's underlying member method code with example usage: obj.create_layer(block, planes, blocks, stride) and returns: nn